Stochastic Methods

On Information Gain and Regret Bounds in Gaussian Process Bandits AISTATS 2021 Online Non-Monotone DR-Submodular Maximization AAAI 2021 Online Risk-Averse Submodular Maximization IJCAI 2021 Online stochastic gradient descent on non-convex losses from high-dimensional inference JMLR 2021 On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization ICML 2021 On Multi-Armed Bandit Designs for Dose-Finding Trials JMLR 2021 On Riemannian Stochastic Approximation Schemes with Fixed Step-Size AISTATS 2021 On the Bias-Variance-Cost Tradeoff of Stochastic Optimization NeurIPS 2021 On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients ICML 2021 On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness COLT 2021 On the Convergence of Stochastic Compositional Gradient Descent Ascent Method IJCAI 2021 On the convergence of the Metropolis algorithm with fixed-order updates for multivariate binary probability distributions AISTATS 2021 On the Rate of Convergence of Regularized Learning in Games: From Bandits and Uncertainty to Optimism and Beyond NeurIPS 2021 On the Second-order Convergence Properties of Random Search Methods NeurIPS 2021 Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm COLT 2021 Optimal Estimation of High Dimensional Smooth Additive Function Based on Noisy Observations ICML 2021 Optimal Gradient-based Algorithms for Non-concave Bandit Optimization NeurIPS 2021 Optimal Order Simple Regret for Gaussian Process Bandits NeurIPS 2021 Optimal Rates for Random Order Online Optimization NeurIPS 2021 Optimal Sketching for Trace Estimation NeurIPS 2021 Optimal Underdamped Langevin MCMC Method NeurIPS 2021 Optimizing Storage Performance with Calibrated Interrupts OSDI 2021 Oracle Complexity in Nonsmooth Nonconvex Optimization NeurIPS 2021 Parallel tempering on optimized paths ICML 2021 Parameter-Free Multi-Armed Bandit Algorithms with Hybrid Data-Dependent Regret Bounds COLT 2021